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The term “big data” has arisen in recent years to describe multi-terabyte data sets. Big data certainly has its challenges relative to scalability and data management. But it’s also useful for business intelligence purposes. In particular, the massive data sets of big data provide substantial data samples for various forms of analytics, especially advanced forms that are discovery oriented.
In a related trend, many organizations are stepping up their use of analytics as a way of understanding changing business environments and discovering new growth opportunities. Furthermore, big data tends to get big because much of it is coming from new sources such as Web sites, social media, mobile devices, robotics, and sensors. Big data from new sources promises new information-driven opportunities—if you know how to leverage big data. Whether you’re trying to discover the root cause of the latest customer churn or define new customer segments and products of affinity that can fuel growth, you need discovery-oriented analytic tools and techniques that work well with big data.
For these reasons, big data and advanced analytics have come together in recent years, causing substantial changes in tools, platforms, business processes, and technical best practices for analytics.
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